In both research fields, Case-Based Reasoning and Reinforcement Learning, the system under consideration gains its expertise from experience. Utilizing this fundamental common ground as well as further characteristics and results of these two disciplines, in this paper we develop an approach that facilitates the distributed learning of behaviour policies in cooperative multi-agent domains without communication between the learning agents. We evaluate our algorithms in a case study in reactive production scheduling. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Gabel, T., & Riedmiller, M. (2006). Multi-agent case-based reasoning for cooperative reinforcement learners. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4106 LNAI, pp. 32–46). Springer Verlag. https://doi.org/10.1007/11805816_5
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